User and Entity Behavior Analytics (UEBA) is a type of cybersecurity solution that uses machine learning to monitor and analyze the behavior of users and entities (such as devices, applications, servers, etc.) in a network. UEBA can detect anomalous or malicious activities in real time and alert security teams or take automated actions.
UEBA solutions work by analyzing activity from network users and other entities, such as hosts, applications, data repositories, and network traffic. They apply machine learning to real-time and historical data to develop a baseline of normal activity. This baseline can then be used to identify any unusual or suspicious behavior.
UEBA can be used to detect a wide range of threats, including:
UEBA is particularly effective at detecting insider threats, which are malicious activities carried out by authorized users. Insider threats can be difficult to detect using traditional security solutions because they often involve legitimate credentials and access. UEBA can identify insider threats by monitoring for unusual user behavior, such as accessing sensitive data at unusual times or from unusual locations.
UEBA is also a valuable tool for detecting malware infections. Malware can often evade traditional security solutions by changing its behavior or hiding its presence. UEBA can detect malware by monitoring for unusual system behavior, such as increased network traffic or CPU usage.
UEBA solutions can be deployed on-premises or in the cloud. They can be integrated with other security solutions, such as security information and event management (SIEM) systems, to provide a more comprehensive and proactive approach to security.
Benefits of UEBA
UEBA offers a number of benefits to organizations, including:
In conclusion, UEBA is a powerful cybersecurity solution that can help organizations improve their threat detection capabilities and reduce their risk of attack. UEBA is particularly effective at detecting insider threats, malware infections, and other advanced attacks.
User entity behavior analytics, otherwise referred to as UEBA, slowly emerged to replace UBA, offering more powerful solutions. As the threat landscape grew, “entities” were added to UBA to monitor malicious behavior beyond the user level. While UBA can detect human behavior within a network, UEBA can model behaviors of humans as well as the machines within networks, including devices, in addition to applications as well as networks, providing complete visibility. When behavioral abnormalities are associated with an entity (i.e. a particular IP address), attacks hardly go unnoticed. By using a baseline of normal user and machine behaviors, UEBA can recognize when a machine is compromised, and thus minimize the amount of damage that can be done.
While they may seem synonymous, UBA and UEBA are distinctly different. While UBA can detect and track suspicious activities and behaviors, UEBA is able to detect abnormalities that are more complex across multiple users, devices, and IP addresses. Unlike UBA, UEBA tracks user activity and other entities. These entities may or may not include managed and unmanaged endpoints, networks, applications, and external threats.
UBA and SIEM (security and information event management) are closely related. UBA tools work in conjunction with SIEM solutions to reveal anomalies in behavioral patterns within a network. To perform analysis, UEBA relies on security data which is collected and stored by a SIEM. UBA works in real time to uncover unknown threats and anomalies, whereas SIEM uses point-in-time analysis, which means that it can only process a limited number of events in a particular time frame. By combining UBA with a SIEM solution, human and machine behavior can both be spotlighted, providing organizations with the benefits of advanced threat detection that traditional security tools often miss.
User behavior can be defined as how users interact with a website. Typically, this can refer to any action a user takes, such as the amount of time they spend on a specific page, how many pages they visit, how long they remain on the clicked pages, which links they click on, how they scroll, when and where they leave the website from, and much more. Tracking user activity can be especially helpful when related to threats or cyberattacks. Detecting potential risks or threats before they escalate can save organizations from experiencing damage to their systems, and can save lots of money and time.
Behavior analytics tools are tools used by an organization for analytics, statistics, data protection, or breach prevention. With the hacking incidents increasing more and more frequently, using behavioral analytic tools has become a crucial element for all businesses. The primary goal of behavior analytics tools is to track a user's behavior and data usage, as well as network events and typical behavior patterns to easily identify potential threats based on detected anomalies.
There are many benefits of using behavior analytics tools. These include:
Below is a list of some key features to consider when choosing a UBA tool:
